require(phyloseq)
Loading required package: phyloseq
require(tidyverse)
Loading required package: tidyverse
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5 ✓ purrr 0.3.4
✓ tibble 3.1.6 ✓ dplyr 1.0.7
✓ tidyr 1.1.4 ✓ stringr 1.4.0
✓ readr 2.1.1 ✓ forcats 0.5.1
── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
require(phyloseq)
require(reshape2)
Loading required package: reshape2
Attaching package: ‘reshape2’
The following object is masked from ‘package:tidyr’:
smiths
require(dplyr)
require(ggplot2)
Load data
ps_dmn <- readRDS("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/data/PhyloseqObjects/16S/DMN_ests_16S.Rdata")
sample_data(ps_dmn)$Herbicide <- factor(sample_data(ps_dmn)$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"))
sample_data(ps_dmn)$herb_time<-paste(sample_data(ps_dmn)$Herbicide, sample_data(ps_dmn)$Time, sep = "_")
ps_rare <- readRDS("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/data/PhyloseqObjects/16S/HerbPt1_rare_16S.Rdata")
sample_data(ps_rare)$Herbicide <- factor(sample_data(ps_rare)$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"))
sample_data(ps_rare)$herb_time<-paste(sample_data(ps_rare)$Herbicide, sample_data(ps_rare)$Time, sep = "_")
ps_trans <- readRDS("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/data/PhyloseqObjects/16S/HerbPt1_hel_trans_16S.Rdata")
sample_data(ps_trans)$Herbicide <- factor(sample_data(ps_trans)$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"))
sample_data(ps_trans)$herb_time<-paste(sample_data(ps_trans)$Herbicide, sample_data(ps_trans)$Time, sep = "_")
create alphadiversity tables
alpha_div <- estimate_richness(physeq = ps_rare, measures = c("Observed", "Shannon", "Chao1"))
#pull out metadata and concatonate with alpha diversity metrics
md<-data.frame(sample_data(ps_rare))
alpha_div_md <- rownames_to_column(alpha_div, "Barcode_ID_G") %>% full_join(md)
Joining, by = "Barcode_ID_G"
alpha_div_md$Herbicide <- factor(alpha_div_md$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"))
Shannon Div plots - no significant differences among herbicide treatments at any of the three time points
ggplot(data = alpha_div_md, aes(Herbicide, Shannon, color= Herbicide)) + facet_grid(. ~ Time) + geom_boxplot() + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1) )
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_Shannon.pdf")
Saving 7.29 x 4.51 in image
aov_t1<-aov(Shannon ~ Herbicide, data = alpha_div_md[alpha_div_md$Time == "T1",])
plot(aov_t1$residuals)
summary(aov_t1)
Df Sum Sq Mean Sq F value Pr(>F)
Herbicide 4 0.0689 0.01723 1.345 0.266
Residuals 51 0.6533 0.01281
aov_t2<-aov(Shannon ~ Herbicide, data = alpha_div_md[alpha_div_md$Time == "T2",])
plot(aov_t2$residuals)
summary(aov_t2)
Df Sum Sq Mean Sq F value Pr(>F)
Herbicide 4 0.0449 0.01123 0.865 0.491
Residuals 49 0.6355 0.01297
aov_t3<-aov(Shannon ~ Herbicide, data = alpha_div_md[alpha_div_md$Time == "T3",])
plot(aov_t3$residuals)
summary(aov_t3)
Df Sum Sq Mean Sq F value Pr(>F)
Herbicide 4 0.0457 0.01142 0.681 0.609
Residuals 50 0.8391 0.01678
ordinations and adonis testing with three separate objects (i.e., dmn, rarefied, transformed). Rare taxa are removed from rarefied and transfomred to sucessfully ordinate. At this point, the transformed data will not ordinate.
ord_dmn<-ordinate(physeq = ps_dmn, method = "NMDS", distance = "bray", k=3, trymax= 300, maxit=1000)
ps_rare_sub<-prune_taxa(taxa_sums(ps_rare) > 2, ps_rare)
ord_rare<-ordinate(physeq = ps_rare_sub, method = "NMDS", distance = "bray", k=3, trymax= 300, maxit=1000)
ps_trans_sub<-prune_taxa(taxa_sums(ps_trans) > 0.05, ps_trans)
ord_transformed<-ordinate(physeq = ps_trans_sub, method = "NMDS", distance = "bray", trymax= 300, maxit=1000)
Adonis testing of herbicide treatments by time point
ps_adonis<-function(physeq){
otu_tab<-data.frame(phyloseq::otu_table(physeq))
md_tab<-data.frame(phyloseq::sample_data(physeq))
if(taxa_are_rows(physeq)== T){
physeq_dist<-parallelDist::parDist(as.matrix(t(otu_tab)), method = "bray")}
else{physeq_dist<-parallelDist::parDist(as.matrix(otu_tab), method = "bray")}
print(anova(vegan::betadisper(physeq_dist, md_tab$Herbicide)))
vegan::adonis(physeq_dist ~ Herbicide * Time, data = md_tab, permutations = 100)
}
ps_adonis(ps_rare_sub)
ps_adonis(ps_trans_sub)
ps_adonis(ps_dmn)
Ordination plots DMN
ord_t1_dmn<-ordinate(physeq = subset_samples(ps_dmn, Time=="T1"), method = "NMDS", distance = "bray", k=3, trymax= 100)
Run 0 stress 0.0860376
Run 1 stress 0.08603731
... New best solution
... Procrustes: rmse 9.373782e-05 max resid 0.0005426049
... Similar to previous best
Run 2 stress 0.08610267
... Procrustes: rmse 0.01568934 max resid 0.1018624
Run 3 stress 0.0867401
Run 4 stress 0.1024122
Run 5 stress 0.08662926
Run 6 stress 0.1030274
Run 7 stress 0.0861028
... Procrustes: rmse 0.01564043 max resid 0.1017144
Run 8 stress 0.08603769
... Procrustes: rmse 0.0001168548 max resid 0.0006317676
... Similar to previous best
Run 9 stress 0.09837316
Run 10 stress 0.097449
Run 11 stress 0.0866292
Run 12 stress 0.08674029
Run 13 stress 0.08673986
Run 14 stress 0.08662905
Run 15 stress 0.08603767
... Procrustes: rmse 0.0008397212 max resid 0.004505676
... Similar to previous best
Run 16 stress 0.0866296
Run 17 stress 0.08610286
... Procrustes: rmse 0.01562942 max resid 0.1016866
Run 18 stress 0.08610292
... Procrustes: rmse 0.01578954 max resid 0.1021695
Run 19 stress 0.08674769
Run 20 stress 0.08662955
*** Solution reached
T1_dmn<-ggordiplots::gg_ordiplot(ord = ord_t1_dmn, groups = data.frame(sample_data(subset_samples(ps_dmn, Time == "T1")))$Herbicide, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1)
T1_dmn$plot + theme_classic()
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_ordination_dmn_T1.pdf")
Saving 7.29 x 4.51 in image
ord_t2_dmn<-ordinate(physeq = subset_samples(ps_dmn, Time=="T2"), method = "NMDS", distance = "bray", k=3, trymax= 100)
Run 0 stress 0.1104159
Run 1 stress 0.1227376
Run 2 stress 0.1095259
... New best solution
... Procrustes: rmse 0.03288404 max resid 0.233578
Run 3 stress 0.1122108
Run 4 stress 0.1095259
... New best solution
... Procrustes: rmse 0.0001772101 max resid 0.0008029145
... Similar to previous best
Run 5 stress 0.1104151
Run 6 stress 0.109526
... Procrustes: rmse 0.0005058391 max resid 0.002638509
... Similar to previous best
Run 7 stress 0.1108795
Run 8 stress 0.1095265
... Procrustes: rmse 0.0008754685 max resid 0.003493245
... Similar to previous best
Run 9 stress 0.1095345
... Procrustes: rmse 0.001373785 max resid 0.006307712
... Similar to previous best
Run 10 stress 0.1122105
Run 11 stress 0.1108802
Run 12 stress 0.1095262
... Procrustes: rmse 0.0002858933 max resid 0.001423687
... Similar to previous best
Run 13 stress 0.1104157
Run 14 stress 0.1104155
Run 15 stress 0.1095258
... New best solution
... Procrustes: rmse 0.0006211951 max resid 0.002413914
... Similar to previous best
Run 16 stress 0.110416
Run 17 stress 0.1095266
... Procrustes: rmse 0.0005856117 max resid 0.002863657
... Similar to previous best
Run 18 stress 0.1183287
Run 19 stress 0.1095259
... Procrustes: rmse 0.0006280806 max resid 0.00242243
... Similar to previous best
Run 20 stress 0.1122104
*** Solution reached
T2_dmn<-ggordiplots::gg_ordiplot(ord = ord_t2_dmn, groups = data.frame(sample_data(subset_samples(ps_dmn, Time == "T2")))$Herbicide, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1)
T2_dmn$plot + theme_classic()
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_ordination_dmn_T2.pdf")
Saving 7.29 x 4.51 in image
ord_t3_dmn<-ordinate(physeq = subset_samples(ps_dmn, Time=="T3"), method = "NMDS", distance = "bray", k=3, trymax= 100)
Run 0 stress 0.09696447
Run 1 stress 0.09696557
... Procrustes: rmse 0.0005621088 max resid 0.003280265
... Similar to previous best
Run 2 stress 0.09692312
... New best solution
... Procrustes: rmse 0.03971579 max resid 0.2647053
Run 3 stress 0.09784875
Run 4 stress 0.09784865
Run 5 stress 0.09838606
Run 6 stress 0.09838755
Run 7 stress 0.09696485
... Procrustes: rmse 0.03976122 max resid 0.2641605
Run 8 stress 0.09701754
... Procrustes: rmse 0.008846055 max resid 0.03955505
Run 9 stress 0.0980118
Run 10 stress 0.09706908
... Procrustes: rmse 0.03980545 max resid 0.2658865
Run 11 stress 0.1008366
Run 12 stress 0.09706926
... Procrustes: rmse 0.03980656 max resid 0.2661383
Run 13 stress 0.1031707
Run 14 stress 0.09838617
Run 15 stress 0.09800986
Run 16 stress 0.09777433
Run 17 stress 0.09696528
... Procrustes: rmse 0.03978925 max resid 0.2640928
Run 18 stress 0.09980988
Run 19 stress 0.09785111
Run 20 stress 0.09696545
... Procrustes: rmse 0.0393462 max resid 0.2628007
Run 21 stress 0.1004913
Run 22 stress 0.09706983
... Procrustes: rmse 0.04026309 max resid 0.2684384
Run 23 stress 0.09696571
... Procrustes: rmse 0.03979867 max resid 0.2654576
Run 24 stress 0.0969422
... Procrustes: rmse 0.003132494 max resid 0.01735126
Run 25 stress 0.09696556
... Procrustes: rmse 0.03991442 max resid 0.2661971
Run 26 stress 0.09785036
Run 27 stress 0.09696507
... Procrustes: rmse 0.03979892 max resid 0.2642904
Run 28 stress 0.09850873
Run 29 stress 0.09696463
... Procrustes: rmse 0.00565883 max resid 0.03678046
Run 30 stress 0.09784824
Run 31 stress 0.09786828
Run 32 stress 0.09696596
... Procrustes: rmse 0.03997918 max resid 0.2659135
Run 33 stress 0.09785936
Run 34 stress 0.09692151
... New best solution
... Procrustes: rmse 0.001159294 max resid 0.006295302
... Similar to previous best
*** Solution reached
T3_dmn<-ggordiplots::gg_ordiplot(ord = ord_t3_dmn, groups = data.frame(sample_data(subset_samples(ps_dmn, Time == "T3")))$Herbicide, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1)
T3_dmn$plot + theme_classic()
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_ordination_dmn_T3.pdf")
Saving 7.29 x 4.51 in image
Ordination plots rarefied
ord_t1_rare<-ordinate(physeq = subset_samples(ps_rare, Time=="T1"), method = "NMDS", distance = "bray", k=3, trymax= 100)
Square root transformation
Wisconsin double standardization
Run 0 stress 0.1810491
Run 1 stress 0.1821958
Run 2 stress 0.1815253
... Procrustes: rmse 0.06152171 max resid 0.2535702
Run 3 stress 0.181072
... Procrustes: rmse 0.02192656 max resid 0.1192016
Run 4 stress 0.180942
... New best solution
... Procrustes: rmse 0.01250884 max resid 0.07370955
Run 5 stress 0.182172
Run 6 stress 0.1815091
Run 7 stress 0.1819124
Run 8 stress 0.1833277
Run 9 stress 0.1823832
Run 10 stress 0.1814063
... Procrustes: rmse 0.02964508 max resid 0.1326549
Run 11 stress 0.1830531
Run 12 stress 0.1837646
Run 13 stress 0.1814152
... Procrustes: rmse 0.03967327 max resid 0.143494
Run 14 stress 0.1837641
Run 15 stress 0.1810727
... Procrustes: rmse 0.0284734 max resid 0.1316853
Run 16 stress 0.1814075
... Procrustes: rmse 0.02961296 max resid 0.1335373
Run 17 stress 0.1839169
Run 18 stress 0.1810711
... Procrustes: rmse 0.0284339 max resid 0.131532
Run 19 stress 0.1812618
... Procrustes: rmse 0.01925932 max resid 0.09503523
Run 20 stress 0.1818106
Run 21 stress 0.1810301
... Procrustes: rmse 0.0257806 max resid 0.1214605
Run 22 stress 0.1813269
... Procrustes: rmse 0.03544089 max resid 0.1387055
Run 23 stress 0.1815286
Run 24 stress 0.187967
Run 25 stress 0.1810728
... Procrustes: rmse 0.02878186 max resid 0.1314131
Run 26 stress 0.1835689
Run 27 stress 0.1814025
... Procrustes: rmse 0.02945674 max resid 0.1322751
Run 28 stress 0.1874015
Run 29 stress 0.1909909
Run 30 stress 0.1814146
... Procrustes: rmse 0.04098914 max resid 0.1452078
Run 31 stress 0.1815991
Run 32 stress 0.181525
Run 33 stress 0.1814023
... Procrustes: rmse 0.02933258 max resid 0.1318085
Run 34 stress 0.1814154
... Procrustes: rmse 0.04114445 max resid 0.1453592
Run 35 stress 0.1814024
... Procrustes: rmse 0.02945012 max resid 0.1321965
Run 36 stress 0.1815255
Run 37 stress 0.1821853
Run 38 stress 0.182388
Run 39 stress 0.1821959
Run 40 stress 0.1828976
Run 41 stress 0.182963
Run 42 stress 0.1812641
... Procrustes: rmse 0.02018832 max resid 0.09662772
Run 43 stress 0.1808181
... New best solution
... Procrustes: rmse 0.01560944 max resid 0.09391373
Run 44 stress 0.1808181
... New best solution
... Procrustes: rmse 0.0001275384 max resid 0.0007245893
... Similar to previous best
*** Solution reached
T1_rare<-ggordiplots::gg_ordiplot(ord = ord_t1_rare, groups = data.frame(sample_data(subset_samples(ps_rare, Time == "T1")))$Herbicide, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1)
T1_rare$plot + theme_classic()
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_ordination_rare_T1.pdf")
Saving 7.29 x 4.51 in image
ord_t2_rare<-ordinate(physeq = subset_samples(ps_rare, Time=="T2"), method = "NMDS", distance = "bray", k=3, trymax= 100)
Square root transformation
Wisconsin double standardization
Run 0 stress 0.1534021
Run 1 stress 0.1501222
... New best solution
... Procrustes: rmse 0.08230186 max resid 0.290523
Run 2 stress 0.1495892
... New best solution
... Procrustes: rmse 0.05350526 max resid 0.2392572
Run 3 stress 0.1502109
Run 4 stress 0.149862
... Procrustes: rmse 0.02211 max resid 0.07569195
Run 5 stress 0.1531695
Run 6 stress 0.1492979
... New best solution
... Procrustes: rmse 0.02816165 max resid 0.1578242
Run 7 stress 0.1547779
Run 8 stress 0.1509426
Run 9 stress 0.1493867
... Procrustes: rmse 0.0124305 max resid 0.06086262
Run 10 stress 0.1493291
... Procrustes: rmse 0.02028147 max resid 0.1029242
Run 11 stress 0.1493185
... Procrustes: rmse 0.01399418 max resid 0.08297848
Run 12 stress 0.1509814
Run 13 stress 0.1502651
Run 14 stress 0.1494113
... Procrustes: rmse 0.01538676 max resid 0.07614067
Run 15 stress 0.1508345
Run 16 stress 0.1532662
Run 17 stress 0.1536907
Run 18 stress 0.1506317
Run 19 stress 0.1533629
Run 20 stress 0.1493236
... Procrustes: rmse 0.01461814 max resid 0.08672645
Run 21 stress 0.1499422
Run 22 stress 0.1558607
Run 23 stress 0.1508846
Run 24 stress 0.151118
Run 25 stress 0.152464
Run 26 stress 0.154282
Run 27 stress 0.1571266
Run 28 stress 0.1500629
Run 29 stress 0.1496188
... Procrustes: rmse 0.01580802 max resid 0.06869795
Run 30 stress 0.1499622
Run 31 stress 0.1529377
Run 32 stress 0.1543548
Run 33 stress 0.1497197
... Procrustes: rmse 0.02742456 max resid 0.1268267
Run 34 stress 0.1494947
... Procrustes: rmse 0.01488277 max resid 0.05659169
Run 35 stress 0.1497881
... Procrustes: rmse 0.01953659 max resid 0.07442972
Run 36 stress 0.1496531
... Procrustes: rmse 0.04709876 max resid 0.1934666
Run 37 stress 0.149388
... Procrustes: rmse 0.01158883 max resid 0.06096413
Run 38 stress 0.1493971
... Procrustes: rmse 0.01766645 max resid 0.1038983
Run 39 stress 0.1502748
Run 40 stress 0.1496127
... Procrustes: rmse 0.02905464 max resid 0.1614178
Run 41 stress 0.1496629
... Procrustes: rmse 0.01445383 max resid 0.05956121
Run 42 stress 0.1493002
... Procrustes: rmse 0.002545233 max resid 0.01386556
Run 43 stress 0.1493066
... Procrustes: rmse 0.0110654 max resid 0.06490723
Run 44 stress 0.1504065
Run 45 stress 0.1502779
Run 46 stress 0.1492988
... Procrustes: rmse 0.008352935 max resid 0.04909222
Run 47 stress 0.151426
Run 48 stress 0.1493351
... Procrustes: rmse 0.02178304 max resid 0.1112067
Run 49 stress 0.1497579
... Procrustes: rmse 0.02964951 max resid 0.1628834
Run 50 stress 0.1493165
... Procrustes: rmse 0.01361412 max resid 0.08043151
Run 51 stress 0.1501407
Run 52 stress 0.1519415
Run 53 stress 0.1511297
Run 54 stress 0.1497795
... Procrustes: rmse 0.02848309 max resid 0.1510366
Run 55 stress 0.1493392
... Procrustes: rmse 0.02236268 max resid 0.1148816
Run 56 stress 0.1502676
Run 57 stress 0.1514846
Run 58 stress 0.1497267
... Procrustes: rmse 0.01883783 max resid 0.08695915
Run 59 stress 0.1492971
... New best solution
... Procrustes: rmse 0.002159199 max resid 0.009525525
... Similar to previous best
*** Solution reached
T2_rare<-ggordiplots::gg_ordiplot(ord = ord_t2_rare, groups = data.frame(sample_data(subset_samples(ps_rare, Time == "T2")))$Herbicide, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1)
T2_rare$plot + theme_classic()
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_ordination_rare_T2.pdf")
Saving 7.29 x 4.51 in image
ord_t3_rare<-ordinate(physeq = subset_samples(ps_rare, Time=="T3"), method = "NMDS", distance = "bray", k=3, trymax= 100)
Square root transformation
Wisconsin double standardization
Run 0 stress 0.1249587
Run 1 stress 0.1259781
Run 2 stress 0.1250301
... Procrustes: rmse 0.01325505 max resid 0.06684092
Run 3 stress 0.1251203
... Procrustes: rmse 0.02363859 max resid 0.07522807
Run 4 stress 0.1256432
Run 5 stress 0.1251302
... Procrustes: rmse 0.01336682 max resid 0.06533419
Run 6 stress 0.1259261
Run 7 stress 0.1259326
Run 8 stress 0.125684
Run 9 stress 0.1266757
Run 10 stress 0.1257443
Run 11 stress 0.1275958
Run 12 stress 0.1266432
Run 13 stress 0.1249629
... Procrustes: rmse 0.003407392 max resid 0.01309949
Run 14 stress 0.1250876
... Procrustes: rmse 0.02158747 max resid 0.07005849
Run 15 stress 0.1256465
Run 16 stress 0.1257186
Run 17 stress 0.1256958
Run 18 stress 0.1250745
... Procrustes: rmse 0.008385728 max resid 0.0374647
Run 19 stress 0.125072
... Procrustes: rmse 0.01857748 max resid 0.07709138
Run 20 stress 0.1250324
... Procrustes: rmse 0.006415311 max resid 0.03741141
Run 21 stress 0.1257058
Run 22 stress 0.1259713
Run 23 stress 0.1266542
Run 24 stress 0.1250275
... Procrustes: rmse 0.006788055 max resid 0.03673037
Run 25 stress 0.1250087
... Procrustes: rmse 0.005547921 max resid 0.01757564
Run 26 stress 0.1250586
... Procrustes: rmse 0.01199068 max resid 0.03664954
Run 27 stress 0.1254315
... Procrustes: rmse 0.01544117 max resid 0.04724371
Run 28 stress 0.1249508
... New best solution
... Procrustes: rmse 0.001496567 max resid 0.004758168
... Similar to previous best
*** Solution reached
T3_rare<-ggordiplots::gg_ordiplot(ord = ord_t3_rare, groups = data.frame(sample_data(subset_samples(ps_rare, Time == "T3")))$Herbicide, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1)
T3_rare$plot + theme_classic()
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_ordination_rare_T3.pdf")
Saving 7.29 x 4.51 in image
box and whisker plots of distance within group distances
library(micrUBIfuns)
beta_boxplot(physeq = subset_samples(ps_rare, Time=="T1"), method = "bray", group = "Herbicide")
$data
$plot
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_T1_rare_withingroup_beta.pdf")
Saving 7.29 x 4.51 in image
beta_boxplot(physeq = subset_samples(ps_rare, Time=="T2"), method = "bray", group = "Herbicide")
$data
$plot
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_T2_rare_withingroup_beta.pdf")
Saving 7.29 x 4.51 in image
beta_boxplot(physeq = subset_samples(ps_rare, Time=="T3"), method = "bray", group = "Herbicide")
$data
$plot
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_T3_rare_withingroup_beta.pdf")
Saving 7.29 x 4.51 in image
treatment to control
plotDistances = function(p, m, s, d) {
# calc distances
wu = phyloseq::distance(p, m)
wu.m = melt(as.matrix(wu))
# remove self-comparisons
wu.m = wu.m %>%
filter(as.character(Var1) != as.character(Var2)) %>%
mutate_if(is.factor,as.character)
# get sample data (S4 error OK and expected)
sd = data.frame(sample_data(p)) %>%
select(s, d) %>%
mutate_if(is.factor,as.character)
sd$Herbicide <- factor(sd$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"))
# combined distances with sample data
colnames(sd) = c("Var1", "Type1")
wu.sd = left_join(wu.m, sd, by = "Var1")
colnames(sd) = c("Var2", "Type2")
wu.sd = left_join(wu.sd, sd, by = "Var2")
#remove this line to plot all comparisons.
wu.sd = wu.sd %>% filter(Type1 == "Hand" | Type1 == "Non-Treated")
# plot
ggplot(wu.sd, aes(x = Type2, y = value)) +
theme_bw() +
geom_point() +
geom_boxplot(aes(color = ifelse(Type1 == Type2, "red", "black"))) +
scale_color_identity() +
facet_wrap(~ Type1, scales = "free_x") +
theme(axis.text.x=element_text(angle = 45, hjust = 1, size = 5)) +
ggtitle(paste0("Distance Metric = ", m))
}
a<-plotDistances(p = subset_samples(physeq= ps_rare, Time=="T1"), m = "bray", s = "Barcode_ID_G", d = "Herbicide")
a <- a + ggtitle("Time 1 Bray-Curtis Dissimlarities")
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_T1_rare_allgroup_beta.pdf")
Saving 7 x 7 in image
b<-plotDistances(p = subset_samples(physeq= ps_rare, Time=="T2"), m = "bray", s = "Barcode_ID_G", d = "Herbicide")
b <-b + ggtitle("Time 2 Bray-Curtis Dissimlarities")
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_T2_rare_allgroup_beta.pdf")
Saving 7 x 7 in image
c<-plotDistances(p = subset_samples(physeq= ps_rare, Time=="T3"), m = "bray", s = "Barcode_ID_G", d = "Herbicide")
c<- c + ggtitle("Time 3 Bray-Curtis Dissimlarities")
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_T3_rare_allgroup_beta.pdf")
Saving 7 x 7 in image
library(ggpubr)
ggarrange(a, b, c, ncol = 1)
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_combined_rare_allgroup_beta.pdf", width = 7, height = 10)
Taxon abundance bar plot
#create super long color vector
col_vector <- c("#000000", "#FFFF00", "#1CE6FF", "#FF34FF", "#FF4A46", "#008941", "#006FA6", "#A30059",
"#FFDBE5", "#7A4900", "#0000A6", "#63FFAC", "#B79762", "#004D43", "#8FB0FF", "#997D87",
"#5A0007", "#809693", "#FEFFE6", "#1B4400", "#4FC601", "#3B5DFF", "#4A3B53", "#FF2F80",
"#61615A", "#BA0900", "#6B7900", "#00C2A0", "#FFAA92", "#FF90C9", "#B903AA", "#D16100",
"#DDEFFF", "#000035", "#7B4F4B", "#A1C299", "#300018", "#0AA6D8", "#013349", "#00846F",
"#372101", "#FFB500", "#C2FFED", "#A079BF", "#CC0744", "#C0B9B2", "#C2FF99", "#001E09",
"#00489C", "#6F0062", "#0CBD66", "#EEC3FF", "#456D75", "#B77B68", "#7A87A1", "#788D66",
"#885578", "#FAD09F", "#FF8A9A", "#D157A0", "#BEC459", "#456648", "#0086ED", "#886F4C",
"#34362D", "#B4A8BD", "#00A6AA", "#452C2C", "#636375", "#A3C8C9", "#FF913F", "#938A81",
"#575329", "#00FECF", "#B05B6F", "#8CD0FF", "#3B9700", "#04F757", "#C8A1A1", "#1E6E00",
"#7900D7", "#A77500", "#6367A9", "#A05837", "#6B002C", "#772600", "#D790FF", "#9B9700",
"#549E79", "#FFF69F", "#201625", "#72418F", "#BC23FF", "#99ADC0", "#3A2465", "#922329",
"#5B4534", "#FDE8DC", "#404E55", "#0089A3", "#CB7E98", "#A4E804", "#324E72", "#6A3A4C",
"#83AB58", "#001C1E", "#D1F7CE", "#004B28", "#C8D0F6", "#A3A489", "#806C66", "#222800",
"#BF5650", "#E83000", "#66796D", "#DA007C", "#FF1A59", "#8ADBB4", "#1E0200", "#5B4E51",
"#C895C5", "#320033", "#FF6832", "#66E1D3", "#CFCDAC", "#D0AC94", "#7ED379", "#012C58",
"#7A7BFF", "#D68E01", "#353339", "#78AFA1", "#FEB2C6", "#75797C", "#837393", "#943A4D",
"#B5F4FF", "#D2DCD5", "#9556BD", "#6A714A", "#001325", "#02525F", "#0AA3F7", "#E98176",
"#DBD5DD", "#5EBCD1", "#3D4F44", "#7E6405", "#02684E", "#962B75", "#8D8546", "#9695C5",
"#E773CE", "#D86A78", "#3E89BE", "#CA834E", "#518A87", "#5B113C", "#55813B", "#E704C4",
"#00005F", "#A97399", "#4B8160", "#59738A", "#FF5DA7", "#F7C9BF", "#643127", "#513A01",
"#6B94AA", "#51A058", "#A45B02", "#1D1702", "#E20027", "#E7AB63", "#4C6001", "#9C6966",
"#64547B", "#97979E", "#006A66", "#391406", "#F4D749", "#0045D2", "#006C31", "#DDB6D0",
"#7C6571", "#9FB2A4", "#00D891", "#15A08A", "#BC65E9", "#FFFFFE", "#C6DC99", "#203B3C",
"#671190", "#6B3A64", "#F5E1FF", "#FFA0F2", "#CCAA35", "#374527", "#8BB400", "#797868",
"#C6005A", "#3B000A", "#C86240", "#29607C", "#402334", "#7D5A44", "#CCB87C", "#B88183",
"#AA5199", "#B5D6C3", "#A38469", "#9F94F0", "#A74571", "#B894A6", "#71BB8C", "#00B433",
"#789EC9", "#6D80BA", "#953F00", "#5EFF03", "#E4FFFC", "#1BE177", "#BCB1E5", "#76912F",
"#003109", "#0060CD", "#D20096", "#895563", "#29201D", "#5B3213", "#A76F42", "#89412E",
"#1A3A2A", "#494B5A", "#A88C85", "#F4ABAA", "#A3F3AB", "#00C6C8", "#EA8B66", "#958A9F",
"#BDC9D2", "#9FA064", "#BE4700", "#658188", "#83A485", "#453C23", "#47675D", "#3A3F00",
"#061203", "#DFFB71", "#868E7E", "#98D058", "#6C8F7D", "#D7BFC2", "#3C3E6E", "#D83D66",
"#2F5D9B", "#6C5E46", "#D25B88", "#5B656C", "#00B57F", "#545C46", "#866097", "#365D25",
"#252F99", "#00CCFF", "#674E60", "#FC009C", "#92896B")
phylumGlommed <- tax_glom(ps_rare, "Phylum")
#t1
phylumGlommed_herb_t1 <- merge_samples(subset_samples(physeq= phylumGlommed, Time=="T1"), group = "Herbicide")
Warning in asMethod(object) : NAs introduced by coercion
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phylumGlommed_herb_t1 <- transform_sample_counts(phylumGlommed_herb_t1, function(OTU) OTU/sum(OTU))
sample_data(phylumGlommed_herb_t1)$Herbicide <- factor(sample_data(phylumGlommed_herb_t1)$Herbicide, levels = c(1, 2, 3, 4, 5),
labels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"))
plot_bar(phylumGlommed_herb_t1, x = "Herbicide", fill = "Phylum") + theme_classic() + ggtitle("Proportional Taxon Abundances Time 1") +
theme(legend.position="bottom") + guides(fill=guide_legend(nrow=6)) + geom_bar(stat="identity") + theme(axis.text.x=element_text(angle = 45, hjust = 1, size = 5)) +
scale_fill_manual(values = col_vector)
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_Taxon_barplot_t1.pdf")
Saving 7.29 x 4.51 in image
#t2
phylumGlommed_herb_t2 <- merge_samples(subset_samples(physeq= phylumGlommed, Time=="T2"), group = "Herbicide")
Warning in asMethod(object) : NAs introduced by coercion
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phylumGlommed_herb_t2 <- transform_sample_counts(phylumGlommed_herb_t2, function(OTU) OTU/sum(OTU))
sample_data(phylumGlommed_herb_t2)$Herbicide <- factor(sample_data(phylumGlommed_herb_t2)$Herbicide, levels = c(1, 2, 3, 4, 5),
labels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"))
plot_bar(phylumGlommed_herb_t2, x = "Herbicide", fill = "Phylum") + theme_classic() + ggtitle("Proportional Taxon Abundances Time 1") +
theme(legend.position="bottom") + guides(fill=guide_legend(nrow=6)) + geom_bar(stat="identity") + theme(axis.text.x=element_text(angle = 45, hjust = 1, size = 5)) +
scale_fill_manual(values = col_vector)
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_Pt1/Figures/16S_Taxon_barplot_t2.pdf")
Saving 7.29 x 4.51 in image
#t3
phylumGlommed_herb_t3 <- merge_samples(subset_samples(physeq= phylumGlommed, Time=="T3"), group = "Herbicide")
Warning in asMethod(object) : NAs introduced by coercion
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phylumGlommed_herb_t3 <- transform_sample_counts(phylumGlommed_herb_t3, function(OTU) OTU/sum(OTU))
sample_data(phylumGlommed_herb_t3)$Herbicide <- factor(sample_data(phylumGlommed_herb_t3)$Herbicide, levels = c(1, 2, 3, 4, 5),
labels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"))
plot_bar(phylumGlommed_herb_t3, x = "Herbicide", fill = "Phylum") + theme_classic() + ggtitle("Proportional Taxon Abundances Time 1") +
theme(legend.position="bottom") + guides(fill=guide_legend(nrow=6)) + geom_bar(stat="identity") + theme(axis.text.x=element_text(angle = 45, hjust = 1, size = 5)) +
scale_fill_manual(values = col_vector)
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_Pt1/Figures/16S_Taxon_barplot_t3.pdf")
Saving 7.29 x 4.51 in image
Combined herbicide and time bar plot for exploration
sample_data(ps_rare)$herb_time<-paste(sample_data(ps_rare)$Herbicide, sample_data(ps_rare)$Time, sep = "_")
ps_rare_for_barplot <- prune_taxa(taxa_sums(ps_rare) > 50, ps_rare)
plot_bar(ps_rare_for_barplot, x= "herb_time", fill = "Family") + scale_fill_manual(values = col_vector) + geom_bar(stat="identity")
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/16S_BarPlot_Herbicide_Time.pdf", width = 20, height = 11)
Linear modeling of abundant taxa
Tax_glom_Subset <- function (physeq, y = "taxLevel", nreturns = "Number of returns"){
ps_1<- tax_glom(ps_rare_sub, taxrank = y )
myTaxa <- names(sort(taxa_sums(ps_1), decreasing = TRUE)[1:nreturns])
ps_1_sub <- prune_taxa(myTaxa, ps_1)
return(ps_1_sub)
}
ps_rare_family_top25<-Tax_glom_Subset(physeq = ps_rare, nreturns = 25, y = "Family")
#explore top 25 taxa with plot bar
plot_bar(ps_rare_family_top25, x= "herb_time", fill = "Family") + scale_fill_manual(values = col_vector) + geom_bar(stat="identity")
#write function to wrangle data prior to anova
abund_aov_wrangle <- function (physeq, y = "Tax_Level"){
tax<-tax_table(physeq)[,y]
meta<-data.frame(sample_data(physeq))
counts<-data.frame(otu_table(physeq))
rownames(counts) <- tax[,1]
counts<-data.frame(t(counts))
counts$Time <- meta$Time
counts$Herbicide <- meta$Herbicide
counts$Herb_time <- meta$herb_time
return(counts)
}
test<-abund_aov_wrangle(ps_rare_family_top25, y = "Family")
mod_abund<-function(count_tab, DV = "Independent Variable") {
for (i in 3:ncol(count_tab)-2) {
for(j in 1:length(unique(count_tab[,"Herbicide"]))){
data <- count_tab %>% filter(Herbicide == unique(count_tab$Herbicide[j]))
mod <- aov(unlist(data[1]) ~ matrix(data[,DV]))
if(summary(mod)[[1]][["Pr(>F)"]][1] <= 0.05) {
data <- count_tab %>% filter(Herbicide == unique(count_tab$Herbicide[j]))
#return(boxplot(unlist(data[1]) ~ matrix(data[,DV]), main = paste(names(data[1])), xlab= "Time", ylab="Abundance")
# )
print(boxplot(unlist(data[1]) ~ matrix(data[,DV]), main =paste(names(data[i]), as.character(unique(count_tab[,"Herbicide"]))[j]), xlab= "Time", ylab="Abundance") )
}
}
}
}
mod_abund(count_tab = test, DV = "Time")
$stats
[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
$conf
[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
$out
f.Micrococcaceae33
141
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[1] 3
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[1] "T1" "T2" "T3"
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[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
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[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
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f.Micrococcaceae33
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[,1] [,2] [,3]
[1,] 22.0 23 38.0
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[3,] 32.0 38 47.0
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[1] 12 11 11
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[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
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f.Micrococcaceae33
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[1,] 22.0 23 38.0
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[4,] 44.5 46 67.5
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[1] 12 11 11
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[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
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$out
f.Micrococcaceae33
141
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[1] 3
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[1] "T1" "T2" "T3"
$stats
[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
$conf
[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
$out
f.Micrococcaceae33
141
$group
[1] 3
$names
[1] "T1" "T2" "T3"
$stats
[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
$conf
[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
$out
f.Micrococcaceae33
141
$group
[1] 3
$names
[1] "T1" "T2" "T3"
$stats
[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
$conf
[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
$out
f.Micrococcaceae33
141
$group
[1] 3
$names
[1] "T1" "T2" "T3"
$stats
[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
$conf
[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
$out
f.Micrococcaceae33
141
$group
[1] 3
$names
[1] "T1" "T2" "T3"
$stats
[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
$conf
[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
$out
f.Micrococcaceae33
141
$group
[1] 3
$names
[1] "T1" "T2" "T3"
$stats
[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
$conf
[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
$out
f.Micrococcaceae33
141
$group
[1] 3
$names
[1] "T1" "T2" "T3"
$stats
[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
$conf
[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
$out
f.Micrococcaceae33
141
$group
[1] 3
$names
[1] "T1" "T2" "T3"
$stats
[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
$conf
[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
$out
f.Micrococcaceae33
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$group
[1] 3
$names
[1] "T1" "T2" "T3"
$stats
[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
$conf
[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
$out
f.Micrococcaceae33
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$group
[1] 3
$names
[1] "T1" "T2" "T3"
$stats
[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
$conf
[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
$out
f.Micrococcaceae33
141
$group
[1] 3
$names
[1] "T1" "T2" "T3"
$stats
[,1] [,2] [,3]
[1,] 22.0 23 38.0
[2,] 26.0 33 40.0
[3,] 32.0 38 47.0
[4,] 44.5 46 67.5
[5,] 61.0 60 73.0
$n
[1] 12 11 11
$conf
[,1] [,2] [,3]
[1,] 23.56203 31.80696 33.89933
[2,] 40.43797 44.19304 60.10067
$out
f.Micrococcaceae33
141
$group
[1] 3
$names
[1] "T1" "T2" "T3"
$stats
[,1] [,2] [,3]
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